diff --git a/analyze.py b/analyze.py
index 348a20e8a..8d62aac5d 100644
--- a/analyze.py
+++ b/analyze.py
@@ -71,42 +71,40 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     return dataframe
 
 
-def populate_trends(dataframe: DataFrame) -> DataFrame:
+def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
     """
-    Populates the trends for the given dataframe
+    Based on TA indicators, populates the buy trend for the given dataframe
     :param dataframe: DataFrame
-    :return: DataFrame with populated trends
-    """
-    """
-    dataframe.loc[
-        (dataframe['stochrsi'] < 20)
-        & (dataframe['close_30_ema'] > (1 + 0.0025) * dataframe['close_60_ema']),
-        'underpriced'
-    ] = 1
+    :return: DataFrame with buy column
     """
     dataframe.loc[
         (dataframe['stochrsi'] < 20)
         & (dataframe['macd'] > dataframe['macds'])
         & (dataframe['close'] > dataframe['sar']),
-        'underpriced'
+        'buy'
     ] = 1
-    dataframe.loc[dataframe['underpriced'] == 1, 'buy'] = dataframe['close']
+    dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
     return dataframe
 
 
 def analyze_ticker(pair: str) -> DataFrame:
+    """
+    Get ticker data for given currency pair, push it to a DataFrame and
+    add several TA indicators and buy signal to it
+    :return DataFrame with ticker data and indicator data
+    """
     minimum_date = arrow.now() - timedelta(hours=6)
     data = get_ticker(pair, minimum_date)
     dataframe = parse_ticker_dataframe(data['result'], minimum_date)
     dataframe = populate_indicators(dataframe)
-    dataframe = populate_trends(dataframe)
+    dataframe = populate_buy_trend(dataframe)
     return dataframe
 
 def get_buy_signal(pair: str) -> bool:
     """
-    Calculates a buy signal based on StochRSI indicator
+    Calculates a buy signal based several technical analysis indicators
     :param pair: pair in format BTC_ANT or BTC-ANT
-    :return: True if pair is underpriced, False otherwise
+    :return: True if pair is good for buying, False otherwise
     """
     dataframe = analyze_ticker(pair)
     latest = dataframe.iloc[-1]
@@ -116,7 +114,7 @@ def get_buy_signal(pair: str) -> bool:
     if signal_date < arrow.now() - timedelta(minutes=10):
         return False
 
-    signal = latest['underpriced'] == 1
+    signal = latest['buy'] == 1
     logger.debug('buy_trigger: %s (pair=%s, signal=%s)', latest['date'], pair, signal)
     return signal
 
@@ -141,7 +139,7 @@ def plot_dataframe(dataframe: DataFrame, pair: str) -> None:
     ax1.plot(dataframe.index.values, dataframe['close_30_ema'], label='EMA(30)')
     ax1.plot(dataframe.index.values, dataframe['close_90_ema'], label='EMA(90)')
     # ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
-    ax1.plot(dataframe.index.values, dataframe['buy'], 'bo', label='buy')
+    ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
     ax1.legend()
 
     ax2.plot(dataframe.index.values, dataframe['macd'], label='MACD')
diff --git a/test/test_analyze.py b/test/test_analyze.py
index 7ba3345ab..48cca6fd5 100644
--- a/test/test_analyze.py
+++ b/test/test_analyze.py
@@ -1,7 +1,7 @@
 # pragma pylint: disable=missing-docstring
 import unittest
 import arrow
-from analyze import parse_ticker_dataframe
+from analyze import parse_ticker_dataframe, populate_buy_trend, populate_indicators
 
 RESULT_BITTREX = {
     'success': True,
@@ -29,6 +29,10 @@ class TestAnalyze(unittest.TestCase):
                           '2017-08-30T10:40:00',
                           '2017-08-30T10:42:00'])
 
+    def test_3_populates_buy_trend(self):
+        dataframe = populate_buy_trend(populate_indicators(self.result))
+        self.assertTrue('buy' in dataframe.columns)
+        self.assertTrue('buy_price' in dataframe.columns)
 
 if __name__ == '__main__':
     unittest.main()